Digital Literacy and Big Data Analytic Tools in Economic Policy Formulation: A PLS-SEM Analysis

Abstract

Big Data Analytics (BDA) tools are becoming increasingly important for evidence-based economic policy-making to monitor economic activity and make informed decisions. But the effective use of tools is contingent on the digital literacy (DL), perceived usefulness (PU) of technology, and implementation challenges. This research investigates the interrelationships between DL, PU, implementation barriers and implementation importance (IM) of BDA tools among economic policy professionals, as well as the mediating effect of PU. A survey of 110 economic policy professionals in Jordan was conducted and tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that DL positively affects PU and IM of BDA tools. Similarly, PU positively affects IM confirming the importance of PU in technology acceptance. In addition, DL directly influences IM revealing that there are direct and indirect behavioural mechanisms at play. Moreover, the research reveals that implementation issues have a negative impact on PU (albeit a small one). Mediation tests reveal that PU partially mediates the digital literacy-IM link. The model’s explanatory power is adequate, with moderate variance explained (R²) for the core endogenous variables. Predictive relevance (Q²) also supports the adequacy of the model, with reliability and validity measures supporting the measurement adequacy. In all, the results identify DL as a major predictor of BDA adoption in economic policy making, while PU serves as an important psychological construct that converts capability into commitment to adoption. This research adds to the technology adoption body of knowledge by incorporating both behavioral and capability approaches in the realm of data-driven governance and economic policy.